Papers by Seong Jae Hwang

1 papers
The Change that Matters in Discourse Parsing: Estimating the Impact of Domain Shift on Parser Error (2022.findings-acl)

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Challenge: Discourse analysis is very low on texts outside of the training distribution’s coverage, diminishing the practical utility of existing models.
Approach: They propose to use a distribution shift statistic to estimate the error-gap of a discourse model and to use it to estimate it.
Outcome: The proposed model can be estimated via distribution shift but does not correlate with change in the observed error of a classifier (i.e. error-gap).

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